Summary of Parallel Momentum Methods Under Biased Gradient Estimations, by Ali Beikmohammadi et al.
Parallel Momentum Methods Under Biased Gradient Estimations
by Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates parallel stochastic gradient methods for solving large-scale machine learning problems on distributed data nodes. While biased gradient estimation is a common issue in many applications, existing research has focused on unbiased gradients. The authors establish worst-case bounds for parallel momentum methods under biased gradient estimation for both non-convex and μ-PL problems, covering general distributed optimization and special cases like meta-learning and compressed/ clipped gradients. Numerical experiments validate the findings, showing faster convergence of momentum methods over traditional biased gradient descent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Parallel machine learning can solve big problems, but it’s tricky when data is spread across many nodes. This paper looks at how to make parallel stochastic gradient methods work better in situations where we don’t have completely unbiased gradients. The authors show that using momentum (a way to speed up the process) with biased gradients can still lead to good results and even beat traditional methods. They tested this on some examples and found it worked well. |
Keywords
* Artificial intelligence * Gradient descent * Machine learning * Meta learning * Optimization
